Literature DB >> 21641269

Mediastinal atlas creation from 3-D chest computed tomography images: application to automated detection and station mapping of lymph nodes.

Marco Feuerstein1, Ben Glocker, Takayuki Kitasaka, Yoshihiko Nakamura, Shingo Iwano, Kensaku Mori.   

Abstract

One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21641269     DOI: 10.1016/j.media.2011.05.005

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  7 in total

1.  A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

Authors:  Holger R Roth; Le Lu; Ari Seff; Kevin M Cherry; Joanne Hoffman; Shijun Wang; Jiamin Liu; Evrim Turkbey; Ronald M Summers
Journal:  Med Image Comput Comput Assist Interv       Date:  2014

2.  2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers.

Authors:  Ari Seff; Le Lu; Kevin M Cherry; Holger R Roth; Jiamin Liu; Shijun Wang; Joanne Hoffman; Evrim B Turkbey; Ronald M Summers
Journal:  Med Image Comput Comput Assist Interv       Date:  2014

3.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

Authors:  Hoo-Chang Shin; Holger R Roth; Mingchen Gao; Le Lu; Ziyue Xu; Isabella Nogues; Jianhua Yao; Daniel Mollura; Ronald M Summers
Journal:  IEEE Trans Med Imaging       Date:  2016-02-11       Impact factor: 10.048

4.  Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest.

Authors:  Jiamin Liu; Joanne Hoffman; Jocelyn Zhao; Jianhua Yao; Le Lu; Lauren Kim; Evrim B Turkbey; Ronald M Summers
Journal:  Med Phys       Date:  2016-07       Impact factor: 4.071

5.  Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software.

Authors:  Sarah M Ryan; Brian Vestal; Lisa A Maier; Nichole E Carlson; John Muschelli
Journal:  Acad Radiol       Date:  2019-12-13       Impact factor: 3.173

6.  Split-bolus contrast injection protocol enhances the visualization of the thoracic vasculature and reduced radiation dose during chest CT.

Authors:  Salah Zein-El-Dine; Imad Bou Akl; Maha Mohamad; Ahmad Chmaisse; Stephanie Chahwan; Karl Asmar; Fadi El-Merhi; Charbel Saade
Journal:  Br J Radiol       Date:  2018-10-01       Impact factor: 3.039

7.  Automated mediastinal lymph node detection from CT volumes based on intensity targeted radial structure tensor analysis.

Authors:  Hirohisa Oda; Kanwal K Bhatia; Masahiro Oda; Takayuki Kitasaka; Shingo Iwano; Hirotoshi Homma; Hirotsugu Takabatake; Masaki Mori; Hiroshi Natori; Julia A Schnabel; Kensaku Mori
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-09
  7 in total

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